136,409 research outputs found
Applying an evolutionary approach for learning path optimization in the next-generation e-learning systems
Learning analytics is targeted to better understand and optimize the process of learning and its environments through the measurement, collection and analysis of learners' data and contexts. To advise people's learning in a specific subject, most intelligent e-learning systems would require course instructors to explicitly input some prior knowledge about the subject such as all the pre-requisite requirements between course modules. Yet human experts may sometimes have conflicting views leading to less desirable learning outcomes. In a previous study, we proposed a complete system framework of learning analytics to perform an explicit semantic analysis on the course materials, followed by a heuristic-based concept clustering algorithm to group relevant concepts before finding their relationship measures, and lastly employing a simple yet efficient evolutionary approach to return the optimal learning sequence. In this paper, we carefully consider to enhance the original evolutionary optimizer with the hill-climbing heuristic, and also critically evaluate the impacts of various experts' recommended learning sequences possibly with conflicting views to optimize the learning paths for the next-generation e-learning systems. More importantly, the integration of heuristics can make our proposed framework more self-adaptive to less structured knowledge domains with conflicting views. To demonstrate the feasibility of our prototype, we implemented a prototype of the proposed e-learning system framework for learning analytics. Our empirical evaluation clearly revealed many possible advantages of our proposal with interesting directions for future investigation. © 2013 IEEE.published_or_final_versio
ââThereâs so much more to it than what I initially thoughtââ: Stepping into researchersâ shoes with a class activity in a first year psychology survey course
In psychology, it is widely agreed that research methods, although central to the discipline, are particularly challenging to learn and teach, particularly at introductory level. This pilot study explored the potential of embedding a student-conducted research activity in a one-semester undergraduate Introduction to Psychology survey course, with the aims of (a) engaging students with the topic of research methods; (b) developing studentsâ comprehension and application of research methods concepts; and (c) building studentsâ ability to link research with theory. The research activity explored shoe ownership, examining gender differences and relationships with age, and linking to theories of gender difference and of consumer identity. The process of carrying out the research and reflecting on it created a contextualized, active learning environment in which students themselves raised many issues that research methods lectures seek to cover. Students also wrote richer assignments than standard first year mid-term essay
Bibliometric Mapping of the Computational Intelligence Field
In this paper, a bibliometric study of the computational intelligence field is presented. Bibliometric maps showing the associations between the main concepts in the field are provided for the periods 1996ĂąâŹâ2000 and 2001ĂąâŹâ2005. Both the current structure of the field and the evolution of the field over the last decade are analyzed. In addition, a number of emerging areas in the field are identified. It turns out that computational intelligence can best be seen as a field that is structured around four important types of problems, namely control problems, classification problems, regression problems, and optimization problems. Within the computational intelligence field, the neural networks and fuzzy systems subfields are fairly intertwined, whereas the evolutionary computation subfield has a relatively independent position.neural networks;bibliometric mapping;fuzzy systems;bibliometrics;computational intelligence;evolutionary computation
Virtual Collaboration in the Online Educational Setting: A Concept Analysis
This study was designed to explore the concept of virtual collaboration within the context of an online learning environment in an academic setting. Rodgersâ method of evolutionary concept analysis was used to provide a contextual view of the concept to identify attributes, antecedents, and consequences of virtual collaboration. Commonly used terms to describe virtual collaboration are collaborative and cooperative learning, group work, group interaction, group learning and teamwork. A constructivist pedagogy, group-based process with a shared purpose, support and web-based technology are required for virtual collaboration to take place. Consequences of virtual collaboration are higher order thinking and learning to work with others.
A comprehensive definition of virtual collaboration is offered as an outcome of this analysis. Clarification of virtual collaboration prior to using it as a pedagogic tool in the online learning environment will enhance nursing education with the changes in nursing curriculum being implemented today. Further research is recommended to describe the developmental stages of the collaborative process among nursing students in online education and how virtual collaboration facilitates collaboration in practice
Benchmark of structured machine learning methods for microbial identification from mass-spectrometry data
Microbial identification is a central issue in microbiology, in particular in
the fields of infectious diseases diagnosis and industrial quality control. The
concept of species is tightly linked to the concept of biological and clinical
classification where the proximity between species is generally measured in
terms of evolutionary distances and/or clinical phenotypes. Surprisingly, the
information provided by this well-known hierarchical structure is rarely used
by machine learning-based automatic microbial identification systems.
Structured machine learning methods were recently proposed for taking into
account the structure embedded in a hierarchy and using it as additional a
priori information, and could therefore allow to improve microbial
identification systems. We test and compare several state-of-the-art machine
learning methods for microbial identification on a new Matrix-Assisted Laser
Desorption/Ionization Time-of-Flight mass spectrometry (MALDI-TOF MS) dataset.
We include in the benchmark standard and structured methods, that leverage the
knowledge of the underlying hierarchical structure in the learning process. Our
results show that although some methods perform better than others, structured
methods do not consistently perform better than their "flat" counterparts. We
postulate that this is partly due to the fact that standard methods already
reach a high level of accuracy in this context, and that they mainly confuse
species close to each other in the tree, a case where using the known hierarchy
is not helpful
Darwin, sexual selection, and the origins of music
Humans devote ample time to produce and perceive music. How and why this behavioral propensity originated in our species is unknown. For centuries, speculation dominated the study of the evolutionary origins of musicality. Following Darwinâs early intuitions, recent empirical research is opening a new chapter to tackle this mystery
Integrating Evolutionary Computation with Neural Networks
There is a tremendous interest in the development of the evolutionary computation techniques as they are well suited to deal with optimization of functions containing a large number of variables. This paper presents a brief review of evolutionary computing techniques. It also discusses briefly the hybridization of evolutionary computation and neural networks and presents a solution of a classical problem using neural computing and evolutionary computing technique
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